Intra- and inter-cave temperatures and locations used by hibernating Indiana bats: contextualizing disturbance, white-nose syndrome, climate, and optimal temperatures
Bibliographic record
Abstract
We completed winter intracave surveys of Indiana bats ( Myotis sodalis Miller and Allen, 1928) over 40 years at seven important hibernacula in Indiana, USA. We documented locations and temperatures used by bats and found no patterns in cave morphology, intracave roost location, or temperature used by bats before or after advent of white-nose syndrome (WNS). Bats hibernated across a continuum of mid-winter temperatures, ranging from 0 to 11 °C. The mean temperature shifted from 5.95 ± 2.35 °C before WNS to 6.72 ± 2.05 °C after. Historically important hibernacula went from heavily populated (e.g., n = 98 250) to zero bats while others went from small populations to large (e.g., n = 2152 to 86 991). There was no clear set of optimal conditions for hibernation, and our data indicate historical determination of microhabitat preference was based on data obtained from distressed populations, many in refugia. Instead, favorable hibernacula provide a continuum of microclimates that meet needs varying by individual and over the season of hibernation. Such hibernacula support healthy individuals and individuals stressed by WNS. Conservation must move beyond a simplified energetics model of hibernation and embrace variable needs of individuals throughout hibernation. This study helps define preferred outcomes when managing thermal regimes of hibernacula.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".